Frequent changes in construction projects pose challenges to design-construction collaboration due to cascading interactions between design changes and field adjustments. Incomplete design information, improper field operations, and unexpected site conditions may result in deviations between as-designed and as-built conditions of building components, which may lead to misalignments between components. Also, such changes may propagate along networks of building elements (e.g. ductworks), and cause cascading effects that are difficult to track. The propagation of design-built deviations among building elements usually requires a significant amount of change coordination efforts among multiple stakeholders. Improper change management could cause reworks, wastes, delays during construction while increasing construction costs. Furthermore, poor change coordination may also create interruptions in decision-making processes during Operations and Maintenance (O&M) phase. O&M planning can become challenging if detailed changes between as-built and as-designed conditions and information about how spatial changes propagate along the spatial and temporal domains are missing. Construction engineers, therefore, have to analyze design changes and field adjustments causing design-built differences and find ways to control the impacts of such changes on project performance.
Recent technological advancements, such as Building Information Modeling (BIM), enabled construction engineers and managers to coordinate design and construction activities of multiple trades involved in a project. Commercial BIM software facilitates the visualization of building elements including Mechanical, Electrical, and Plumbing (MEP) systems for coordination purposes so that potential clashes among building elements can be resolved virtually before constructability problems occur on site. Some BIM tools support the comparison of multiple versions of as-designed models to detect changes between versions and record design change histories for change management. However, manual updates of as-designed BIM could be error-prone and may miss certain spatial changes occurring in the field. As a result, only using design-oriented BIM tools could hardly track differences between as-designed and as-built conditions.
Several researchers explored the potential of using three-dimensional (3D) imaging technologies, namely 3D laser scanning and photogrammetry coupled with computer vision, for change analysis between as-designed and as-built conditions. 3D laser scanning is an emerging technology that can capture very accurate as-built geometries promptly. Data produced by 3D laser scanners is in the form of dense 3D point clouds. Such point clouds can be used to detect differences between as-designed models and as-built conditions. However, associating objects from the as-designed model with points in point clouds in an efficient and reliable manner is challenging, especially when spatial changes occur. Tang et al. identified the challenges associated with detecting and classifying spatial changes during design and construction processes. That study concluded that a robust spatial change detection and classification approach would enable reliable automatic diagnosis of the propagative effects of changes that cause reworks and construction quality problems. Recent studies have explored the application of relational graphs to match and compare objects from 3D as-designed models with the objects in the corresponding 3D as-built model accurately, which has significant advantages over data-model comparison tools that are available in commercial 3D data processing and reverse engineering environments, such as InnovMetric Polyworks. However, comparing relational graphs generated from as-designed models and 3D laser scan data of large-scale building systems (e.g., hundreds of interconnected ductworks) involves computational complexity that grows exponentially with the number of building elements.
BIM technology addresses the difficulties associated with design change coordination by enabling synchronization of multiple trade design models in a central BIM for clash detection and coordination. Langroodi & Staub-French conducted a case study to exploit the benefits of using BIM for design change management of a fast-track project. Akinci and Boukamp concluded that BIM can document different design alterations, but could hardly address the propagative impacts of changes that collectively influence the construction quality, cost, and productivity. Also, BIM tools mainly focus on design change coordination, while engineers are required to update as-designed BIM manually according to the as-built conditions to analyze the impact of field changes on the project performance. This practice is tedious and error-prone.
Previous studies focused on automated modeling of as-built pipelines from laser scan data for construction quality assessment and monitoring purposes. Construction project managers would use these as-built models to investigate any dimensional deviations between the individual objects of the as-built and as-designed models. Several studies investigated the integrated use of 3D imaging technologies and BIM for detecting and analyzing spatial changes that occur in the field. Tang et al. reviewed a broad range of algorithms and techniques that are used for the recognition and reconstruction of building elements from 3D laser scan data for as-built modeling. Based on this review, Xiong et al. developed methods that automatically create semantically rich BIM from 3D laser scan data using voxel representation to make the as-designed and as-built BIM comparison more efficient. Similar concepts inspired a study that developed an approach for automated spatial change analysis of linear building elements. Bosche developed a robust point matching method for as-built dimension calculation and control of 3D CAD model objects recognized in laser scans. Based on this work, Turkan et al. developed an automated progress monitoring system that combines 4D BIM and 3D laser scan data for change detection and management. Nahangi and Haas developed an automated deviation detection approach for pipe spools based on scan-to-BIM registration. This study employed an automated registration step for quantifying the deviations in the defective parts of the pipe spool assemblies. Bosche et al. coupled Scan-versus-BIM, and Scan-to-BIM approaches to track and diagnose changes of densely packed cylindrical MEP (Mechanical, Electrical, and Plumbing) elements.
The majority of the studies described above utilizes nearest-neighbor searching algorithms for detecting spatial deviations and changes between as-designed and as-built conditions and thus inherit the limitations of this algorithm. In many cases, especially when several similar objects packed in small spaces (e.g., several ducts packed in a mechanical room), the change detection results of nearest neighbor searching may contain mismatches that associate data points with the wrong objects in the as-designed model. As a result, the change analysis and progress monitoring results would be misleading.
Manual comparison of 2D and 3D imagery data against as-designed models is also tedious and error-prone. The majority of the previous change detection studies relied on the “nearest-neighbor searching” paradigm to associate the as-designed model with as-built data. The nearest neighbor searching approach associates each point in a 3D laser scan data with an as-designed model object that is the “nearest neighbor.” In other words, the algorithm considers that each as-built data point in the 3D laser scan data belongs to the object that is in its neighborhood, and the algorithm takes the closest object as the object that corresponds to these points. The nearest neighbor search algorithm then calculates the distances between the corresponding as-designed model objects and as-built data points, and visualize these distances using a color-coded “deviation map.” Such a deviation map highlights the parts that data points are deviating away from their nearest as-designed model objects. Nevertheless, the nearest neighbor searching approach has several limitations that may lead to data-model mismatches. More specifically, nearest neighbor searching could fail to provide reliable results when associating a large number of similar and small objects packed in relatively small spaces, such as mechanical rooms of large facilities. FIG. 1 provides an example to illustrate these limitations. In this case, the ducts in as-built data are associated with the wrong ducts in the as-designed model because of the misalignment between the ducts in the as-designed model and as-built model. This observation indicates that the nearest neighbor searching algorithm failed to accurately associate ducts that were subjected to changes between the as-built and as-designed models.
Another algorithm matches “spatial contexts” of building components, e.g. ducts, captured in as-designed and as-built models to achieve more reliable association between as-designed model and as-built data. The algorithm first constructs “relational graphs” that depict spatial relationships between objects extracted from as-designed models or as-built models created based on 3D laser scan data. More specifically, a relational graph is a network representation of the objects in a model, in which the nodes represent the objects and the edges connecting them represent spatial relationships between objects (FIG. 2). Each node can have attributes to describe the properties of the object, called “local attributes” (e.g., shape, size, or color). The spatial relationships of an object with other objects represent the “spatial context” of that object. After obtaining two relational graphs that respectively represent the as-designed model and the as-built model, the algorithm matches these two relational graphs and associate as-designed objects with as-built model elements (e.g., surfaces and lines extracted from laser scan data) based on the similarity of their attributes and spatial contexts. These two studies showed that this relational-graph-based approach could achieve automatic and reliable change detection of relatively small ductworks (<20 ducts) in a mechanical room.
These two studies described above used cases that involve tens of ducts to validate the relational-graph-based approach. Unfortunately, the computational complexity of extracting and matching relational graphs from large datasets increase exponentially with the number of objects in the as-designed and as-built models. A step forward is thus improving the computational efficiency of the relational-graph-based approach.
It is with these observations in mind, among others, that various aspects of the present disclosure were conceived and developed.
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